An epidemiologist studies the distribution and determinants of diseases in human populations. They identify the causes of diseases and evaluate preventive and therapeutic measures. There are two main types of epidemiology - descriptive epidemiology examines disease distribution while analytic epidemiology tests hypotheses about disease causes and interventions. Observational studies include case-control studies, cohort studies, cross-sectional studies, and ecological studies. Epidemiologists measure disease occurrence through morbidity (prevalence and incidence) and mortality. Prevalence is the proportion of a population with a disease, while incidence is the proportion developing a disease over time.

1. UNDERSTANDING EPIDEMIOLOGY STUDY IN
MEDICAL STATISTICS
by
Laud Randy Amofah
December 2019
Who is an Epidemiologist?
An epidemiologist is someone who studies the distribution of diseases within populations of
people and factors related to them. Epidemiologist analyzes what causes disease outbreaks in
order to treat existing diseases and prevent future outbreaks.
What are some of the things an epidemiologist is interested in studying?
1. Epidemiologists identify the cause of disease and determine the extent of disease.
2. Epidemiologists evaluate preventive and therapeutic measures for a disease or condition.
3. Epidemiologists determine the crucial difference between those who get the disease and those
who are spared.
4. Epidemiologists study exposed and non-exposed people.
5. Epidemiologists also determine the crucial effect of the exposure.
The difference between the two broad types of epidemiology.
Descriptive epidemiology examines the distribution of disease in a population and observes the
basic features of its distribution.
Analytic epidemiology tests a hypothesis about the causes of disease, the effectiveness of
interventions, and showing the determinants of these events by studying how exposures relate to
disease.

2. Using examples to distinguish between the four forms of Observational study
in epidemiology.
a) Case-Control - A case-control is a type of observational that examines multiple exposures in
relation to an outcome; subjects are defined as cases and controls, and exposure histories are
compared. It identifies the cases (a group known to have the outcome) and the controls (a group
known to be free of the outcome).
For example; In 1993, the National Institute of Environmental Health Sciences funded a study in
Iowa regarding the possible relationship between radon levels and the incidence of cancer. The
study gathered information from 413 participants who had developed lung cancer and compared
those results with 614 participants who did not have lung cancer.
b) Cohort studies - Examines multiple health effects of exposure; subjects are defined according
to their exposure levels and followed over time for outcome occurrence.
For example; A recent article in the BBC News Health section described a study concerning
dementia and "mid-life ills". According to the article, researches followed more than 11,000
people over a period of 12-14 years. They found that smoking, diabetes, and high blood pressure
were all factors in the onset of dementia.
c) Cross-sectional studies – Involves looking at data from a population at one specific point in
time.
For example; In 2004, researchers published an article in the New England Journal of
Medicine regarding the relationship between the mental health of soldiers exposed to combat
stress. The study collected information from soldiers in four combat infantry units either before
their deployment to Iraq or three to four months after their return from combat duty.
d) Ecological studies – This is an observational study defined by the level at which data are
analyzed, namely at the population or group level, rather than the individual level. Ecological
studies are often used to measure the prevalence and incidence of disease, particularly when the
disease is rare.
For example; exposure and risk factors are known only at the group level, such as the average air
pollution concentration in different cities. The occurrence of the health outcome may also be

3. only known at the group level, such as overall mortality rates from chronic lung disease in the
same cities with measured levels of air pollution.
Measuring Disease Occurrence
Morbidity: The incidence of disease, as a rate of a population that is affected. The measure of
Morbidity is Prevalence and Incidence.
Mortality: The death rate of a population. The measure of Mortality Incidence.
Defining Prevalence;
Is the proportion of a specific population having a particular disease. Let prevalence denote as p,
p is a number between 0 and 1. If multiplied by 100 it is a percentage.
Defining Incidence;
Is the proportion of a specific, disease-free population
developing a particular disease in a specific study period. Let incidence denote as I, I is a
number between 0 and 1. If multiplied by 100 it is a percentage.
Example 1;
In a school with a population of 3052, there have occurred 11 cases of skin
cancer. An epidemiologist is studying the case.
i. What quantity can be used in measuring the disease occurrence in the
school?
Answer: Prevalence
ii. Compute the quantity for the measure of skin cancer occurrence.
Let prevalence denote p
p = 11/3052
p = 0.0036
p = 0.0036 * 100
p = 0.36%

4. iii. Construct an 80% confidence interval for the quantity computed.
𝑝̂= 0.0036
Var(𝑝̂) =
𝑝(1 − 𝑝̂)
n
Var(𝑝̂) =
0.0036 (1 −0.0036)
3052
Var(𝑝̂) = 0.000001176
SD(𝑝̂) = √𝑉𝑎𝑟(𝑝̂)
SD(𝑝̂) = √0.000001176
SD(𝑝̂) = 0.001084
80% CI is given by:
𝑝̂ ± Za/2 × SD(𝑝̂)
0.0036 ± 1.282× 0.001084
(0.00221,0.00499)
Example 2;
In a myopia-free rural community of 1000 adults, there have occurred 19
new cases of myopia within 3 years.
i. What quantity can an epidemiologist use in measuring the myopia occurrence in the
rural community?
Answer: Incidence
ii. Compute the quantity
Let I denote Incidence
I = 19/1000
I = 0.019
I = 0.019 * 100
I = 1.9%

5. iii. Construct a 95% CI for the quantity.
𝐼̂= 0.019
Var(𝐼̂) =
𝐼(1 − 𝐼̂)
n
Var(𝐼̂) =
0.019 (1 −0.019)
1000
Var(𝐼̂) = 0.0000186
SD(𝐼̂) = √ 𝑉𝑎𝑟(𝐼̂)
SD(𝐼̂) = √0.0000186
SD(𝐼̂) = 0.00431
95% CI is given by:
𝐼̂ ± Za/2 × SD(𝐼̂)
0.019 ± 1.96× 0.00431
(0.0106,0.027)
Example 3;
In the rural community of Keti, the prevalence of malaria was 6.75% in 2017. In
2018, the population of Keti increased by 320 and the new cases of malaria
recorded was 30. Using the above information [hint: population for Keti in
2018 was 1200]
i. Compute the current incidence rate of malaria.
Let I denote Incidence
I = 30/1200
I = 0.025
I = 0.025 * 100
I = 2.5%

6. ii. Construct a 95% CI for the incidence rate.
𝐼̂= 0.025
Var(𝐼̂) =
𝐼(1 − 𝐼̂)
n
Var(𝐼̂) =
0.025(1 −0.025)
1200
Var(𝐼̂) = 0.0000203
SD(𝐼̂) = √ 𝑉𝑎𝑟(𝐼̂)
SD(𝐼̂) = √0.0000203
SD(𝐼̂) = 0.00506
95% CI is given by:
𝐼̂ ± Za/2 × SD(𝐼̂)
0.025 ± 1.96× 0.00506
(0.01508,0.03491)
iii. Compute the current prevalence rate of malaria.
Let p be the prevalence in 2017
Population of Keti in 2017 = 1200 – 320 = 880
p = 6.75/100
p = 0.0675
𝑝 =
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑒𝑜𝑝𝑙𝑒 𝑤𝑖𝑡ℎ 𝑚𝑎𝑙𝑎𝑟𝑖𝑎
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑒𝑜𝑝𝑙𝑒 𝑖𝑛 𝑡ℎ𝑒 𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑒𝑜𝑝𝑙𝑒 ℎ𝑎𝑣𝑖𝑛𝑔 𝑚𝑎𝑙𝑎𝑟𝑖𝑎 𝑖𝑛 2017 = 880 × 0.0675 = 59.4 ≈ 60
New cases in 2018 =30

7. Current number of malaria cases = 60+30 = 90
Current prevalence = 90/1200= 0.075
iv. Construct a 95% CI for the prevalence rate.
𝑝̂= 0.075
Var(𝑝̂) =
𝑝(1 − 𝑝̂)
n
Var(𝑝̂) =
0.075 (1 −0.075)
1200
Var(𝑝̂) = 0.0000578125
SD(𝑝̂) = √𝑉𝑎𝑟(𝑝̂)
SD(𝑝̂) = √0.0000578125
SD(𝑝̂) = 0.007603
95% CI is given by:
𝑝̂ ± Za/2 × SD(𝑝̂)
0.075 ± 1.96× 0.007603
(0.0601, 0.0889)
Example 4;
2x2 Contingency table
Case No Case Total
Exposed 52 213 265
Non-Exposed 9 116 125
Total 61 329 490
Compute the following:

8. i. P(Exposed)
P(Exposed) = 265/490
P(Exposed) = 0.541
ii. P(Case)
P(Case) = 61/490
P(Case) = 0.124
iii. P(Exposed| Case)
P(Exposed| Case) = 52/61
P(Exposed| Case) = 0.852
iv. P(Case | Exposed)
P(Case| Exposed) = 52/265
P(Case| Exposed) = 0.196
v. P(Non-Exposed | Cases)
P(Non-Exposed| Case) = 9/61
P(Non-Exposed| Case) = 0.148
vi. P(No Case)
P(No Case) = 329/490
P(No Case) = 0.671